Feng Li

Agricultural carbon emissions are one of the major causes of global climate change. As some of the world’s largest agricultural producers and consumers, countries along the route of the Belt and Road initiative produce significant agricultural carbon emissions. An in-depth study on the efficiency of agricultural carbon emissions in countries along the route can help countries reduce environmental load while improving agricultural production, optimizing resource use, improving agricultural production efficiency, and achieving sustainable development goals, which is significant for global climate change mitigation. Based on the relational data and network perspective, this paper takes the agricultural carbon emission efficiency of 34 countries along the route of the Belt and Road Initiative from 1995 to 2020 as the research object. It integrates the social network analysis method and other methods to realize the expansion of agricultural carbon emission efficiency in the research method. The study shows that (1) agricultural carbon emission efficiency has more room for improvement and presents complex spatially linked network characteristics; (2) the spatial correlation network of agricultural carbon emission efficiency is relatively well connected, and there is a general spatial correlation and spatial spillover effect among countries; and (3) similar differences in the proportion of primary industries and differences in informatization levels help establish spatial correlations between regions and produce spatial spillover effects. It is imperative to change global economic growth, social development, and lifestyles through green development. This study is conducive to the international community’s formulation of differentiated agricultural carbon emission reduction support mechanisms for different countries to help the countries realize the transformation of agriculture and even overall economic development as soon as possible. At the same time, accelerating the pace of emission reduction and reducing the negative impact of agricultural carbon emissions are conducive to better responding to the challenges posed by global climate change.

With global warming, China’s agricultural products are facing severe production conditions and a complex international trade situation. In order to clarify the relationship between climate change and China’s agricultural trade, this paper uses the GTAP model to explore the impact of climate change on China’s agricultural trade from the perspectives of agricultural production and supply, energy substitution and trade policy. The results show that: (1) From the overall effect, the production supply risk and energy substitution risk caused by climate change have a positive impact on China’s import trade, among which the energy substitution risk has brought about an import trade growth of 38.050%, the production supply risk has brought about an import trade growth of 12.635%, and the trade policy risk has a negative impact, bringing about an import trade decline of 12.589%. (2) Under the impact of production and supply risks caused by climate change, the import volume of different industrial sectors has increased by varying degrees, including livestock products (16.521%) > food crops (14.162%) > cash crops (7.220%). The increase in import trade mainly comes from the United States (10.731%), Canada (10.650%) and Australia (9.455%). (3) Under the impact of energy substitution risk caused by climate change, the increase in import trade was concentrated in food crops (48.144%) and livestock products (42.834%), mainly from the United States (57.098%), the European Union (55.014%) and Canada (53.508%). (4) Under the impact of trade policy risks caused by climate change, the import trade of different industrial sectors showed a downward trend, with cash crops (13.039%) > livestock products (12.588%) > cash crops (12.140%). The countries and regions with significant decline in import trade were ASEAN (−46.131%) and the United States (−28.028%). The trade deficit shifted to surplus, and the terms of trade were improved. Therefore, this paper suggests that we should deal with the impact of climate change on agricultural trade by developing “climate smart” agriculture, actively responding to low-carbon trade measures, and establishing an agricultural trade promotion mechanism to address the risk of climate change.

Whether the quality of agricultural products is safe or not is related to issues of food safety and low carbon production in agriculture. Based on evolutionary game theory, this paper establishes a game model among government, farmers and consumers and analyzes the dynamic evolutionary process and evolutionary stable strategies of the major stakeholders. The results show that: (i) government subsidy coefficient, farmers’ penalty coefficient for not producing, consumer trust coefficient and willingness to pay carbon labelled agricultural products premium are positively related to farmers’ adoption of low-carbon production behaviour, and fraud penalty coefficient and farmers’ cost of adopting low-carbon production technology are negatively related; (ii) farmers’ sensitivity to government regulation policies is: fraud penalty coefficient = farmers’ cost of adopting low-carbon production technology > government subsidy The sensitivity of farmers to government regulation policies is: fraud penalty coefficient = cost of low-carbon production technology > government subsidy > penalty coefficient for non-production, and the sensitivity of farmers to direct market stimulation is: consumer trust coefficient > coefficient of willingness to pay premium for carbon labelled agricultural products, and the additional benefit coefficient has no significant effect on farmers’ decision-making; (iii) In the early stage, the source control of food safety mainly depends on the government’s policy intervention. In the later stage, the establishment of carbon label agricultural products market incentive mechanism can achieve long-term stable and effective source control of food safety.

Achieving transport connectivity is a priority in China’s “Belt and Road Initiative”. In order to further understand the impact of railway infrastructure on city-level economic expansion, we set cities with high-speed rail as the treatment group and those without high-speed rail as the control group, and a difference-in-differences (DID) technique was used to estimate the growth impact and heterogeneity of high-speed rail opening on the economic growth of cities along the New Silk Road Economic Belt. The main results are as follows: First, economic growth in cities with operational high-speed rail lines was significantly higher than those without high-speed rail. Second, the impact of high-speed rail on economic growth exhibited distinct heterogeneity. Large cities tend to have a stronger siphoning effect, resulting in more pronounced impact of high-speed rail opening on urban economic growth. Third, cities with higher marketization levels and higher government efficiency were shown to have stronger economic growth effect.

Due to the importance of understanding the relationship between agricultural growth and environmental quality, we analyzed how high-quality agricultural development can affect carbon emissions in Northwest China. Based on the concept of the environmental Kuznets curve, this study uses provincial panel data from 1993 to 2017 to make empirical analyses inflection point changes and spatio-temporal differences in agricultural carbon emissions. The highlights of our findings are as follows: (1) In Northwest China, there is an inverse N-shape curve, and the critical values are 3578 yuan/hm² and 45,738 yuan/hm², respectively. (2) For 2017, the agricultural economic intensity was 50,670 yuan/hm², exceeding the critical value (high inflection point) of 45,738 yuan/hm². (3) Ningxia, Gansu, and Qinghai have not reached the turning point. Having comparable climate, natural conditions, and overall environmental factors, these three provinces would reach the turning point at similar time periods. (4) The average value in agricultural carbon emission intensity in the region is 767.79 kg/hm², and the order based on intensity is Xinjiang > Shaanxi > Ningxia > Gansu > Qinghai.